Logic and Machine Learning: Laboratory Material

This repository contains the Jupyter notebooks for the Logic and Machine Learning course laboratory sessions. Have fun! 😄

Setup

Julia Programming Language

First of all, this course will be held using the Julia programming language: hurray!

For a taste of the advantages and disadvantages of this language, we suggest reading this summary taken from a famous Julia course, taught at the Czech Technical University in Prague. Summing it up in a few words, Julia is fast and keeps growing in popularity year after year.

To install the latest stable version of Julia, we refer you to the official documentation.

VS Code

Next, we need a good text editor to support the Julia workflow.

The solution we suggest is to download Visual Studio Code, as it is easy to setup and provides a robust extension for handling everything we need.

You can install VS Code from its official website, then open it. Select "View" and then click "Extensions". Enter the keyword "Julia" and download the extension with the same name, maintained by the "julialang" organization. Finally, restart VS Code.

This Repository

Last but not least, we need to download this repository.

This contains a Project.toml file, which is necessary to locally setup a self-contained Julia project and accommodate all the dependencies we are going to need.

The notebooks folder contains the laboratory material, in the form of Jupyter notebooks (the files with ipynb extension), which is the standard for writing, sharing, and reproducing scientific computing scripts.

To download the project, click the green "<>Code" button in the GitHub page of the course and select "Download ZIP". Then, unzip the folder where you prefer on your system. Open VS Code, select "File", "Open Folder" and browse the unzipped folder.

If you have git installed on your machine and are familiar with Unix-shell, you can just run this in the terminal:

git clone https://github.com/aclai-lab/logic-and-machine-learning
cd logic-and-machine-learning
code .

You can toggle a terminal within VS Code, by pressing CTRL + J or by clicking "View" and selecting "Terminal".

Write julia in the terminal to start a new Julia session.

We want to activate the project (we specify to Julia that we are working inside this folder) and instantiate it (we install all the dependencies). To do so, we can exploit the built-in package manager Pkg as follows:

using Pkg
Pkg.activate(".")
Pkg.instantiate()

This will install all dependencies, including:

• IJulia.jl: Julia backend for Jupyter Notebook;
• MLJ.jl: general-purpose machine learning framework;
• SoleLogics.jl: library containing the base definitions for playing with (modal) logic;
• ModalDecisionTrees.jl: machine learning algorithm for extracting modal decision trees.

To open the notebooks, start Jupyter Notebook with the Julia backend by typing:

using IJulia; IJulia.notebook(; dir=".")